An overview of recent advances and challenges in predicting compound-protein interaction (CPI).

Compound-protein interactions (CPIs) are critical in drug discovery for identifying therapeutic targets, drug side effects, and repurposing existing drugs. Machine learning (ML) algorithms have emerged as powerful tools for CPI prediction, offering notable advantages in cost-effectiveness and efficiency. This review provides an overview of recent advances in both structure-based and non-structure-based CPI prediction ML models, highlighting their performance and achievements. It also offers insights into CPI prediction-related datasets and evaluation benchmarks. Lastly, the article presents a comprehensive assessment of the current landscape of CPI prediction, elucidating the challenges faced and outlining emerging trends to advance the field.

Computing the relative binding affinity of ligands based on a pairwise binding comparison network.

Structure-based lead optimization is an open challenge in drug discovery, which is still largely driven by hypotheses and depends on the experience of medicinal chemists. Here we propose a pairwise binding comparison network (PBCNet) based on a physics-informed graph attention mechanism, specifically tailored for ranking the relative binding affinity among congeneric ligands. Benchmarking on two held-out sets (provided by Schrödinger and Merck) containing over 460 ligands and 16 targets, PBCNet demonstrated substantial advantages in terms of both prediction accuracy and computational efficiency. Equipped with a fine-tuning operation, the performance of PBCNet reaches that of Schrödinger's FEP+, which is much more computationally intensive and requires substantial expert intervention. A further simulation-based experiment showed that active learning-optimized PBCNet may accelerate lead optimization campaigns by 473%. Finally, for the convenience of users, a web service for PBCNet is established to facilitate complex relative binding affinity prediction through an easy-to-operate graphical interface.

Impact of particle size and pH on protein corona formation of solid lipid nanoparticles: A proof-of-concept study.

When nanoparticles were introduced into the biological media, the protein corona would be formed, which endowed the nanoparticles with new bio-identities. Thus, controlling protein corona formation is critical to in vivo therapeutic effect. Controlling the particle size is the most feasible method during design, and the influence of media pH which varies with disease condition is quite important. The impact of particle size and pH on bovine serum albumin (BSA) corona formation of solid lipid nanoparticles (SLNs) was studied here. The BSA corona formation of SLNs with increasing particle size (120-480 nm) in pH 6.0 and 7.4 was investigated. Multiple techniques were employed for visualization study, conformational structure study and mechanism study, etc. "BSA corona-caused aggregation" of SLN2‒3 was revealed in pH 6.0 while the dispersed state of SLNs was maintained in pH 7.4, which significantly affected the secondary structure of BSA and cell uptake of SLNs. The main interaction was driven by van der Waals force plus hydrogen bonding in pH 7.4, while by electrostatic attraction in pH 6.0, and size-dependent adsorption was confirmed. This study provides a systematic insight to the understanding of protein corona formation of SLNs.